We conduct experiments on S3DIS [1]} for large-scale indoor scene segmentation. S3DIS [1]} is a challenging dataset which consists of point clouds collected from six areas. Following the convention [3]}, [4]}, we leave out area 5 for testing purpose. For data preparation, we split rooms into \(2m\times 2m\)  blocks, with \(0.5m\)  padding along each side \((x,y)\) . These context points do not involve in neither loss computation nor prediction during testing. We use color as input feature, point coordinates are incorporated in the network for voxelization and local aggregation. At training time, we random sample 8192 points from block data, and use original points at testing time.
